The insights provided by neurobiologist Dale Purves and his colleagues over the last few years about why the brain doesn't see the world according to the measurements provided by rulers, protractors or photometers suggest that vision operates in way very different from what most neuroscientists imagine.
In a new book " Perceiving Geometry: Geometric Illusions Explained by Natural Scene Statistics" (Springer), Purves and colleague Catherine Howe explore why the brain generates geometric illusions.
Visual perception is a daunting task for the brain, explains Purves, because light streaming into the eye carries only ambiguous information about the environment.
"The basic problem, recognized for several centuries, is that the image on our retinas can't specify what's out there in the world," said Purves. "The light received by our retinal receptors tangles up illumination, reflectance, transmittance, size, distance and orientation," said Purves. "This means that there's no logical way to get back from the retinal image to what's actually out there in the world."
Nevertheless, many neurobiologists have attempted to explain vision by postulating that the brain's neural wiring can definitively "calculate" the features of a visual scene, despite the visual world's inevitable ambiguity. Such "rule-based" theories, said Purves, have arisen because neurobiologists have concentrated on understanding how neurons in the brain's visual region extract and recognize specific features such as edges in a visual scene.
"Because of the enormouspower and success of modern neurophysiology and neuroanatomy, there just didn't seem to be any reason to think much about this issue," said Purves. "However, we began worrying about it seven or eight years ago because the physiology and anatomy people had described didn't explain what we end up seeing. There was no instance, even in the simplest aspects of vision, where the properties of visual neurons in the brain explain the brightness, colors or forms that we actually see."
Thus, Purves and his colleagues began exploring visual illusions -- the name given to the more obvious discrepancies between the physical world and the way people see it -- to understand the strategy the brain uses in perceiving the world. Basically, they statistically compared perceptions -- such as the apparent length of a line -- with physical measurements of what the line stimulus on the retina was most likely to represent in the real world.
This sort of analysis, made by measuring a large set of geometrical images with a device called a laser range scanner, showed that the brain is not a calculating engine, cranking out stimulus features, but a "statistical engine" wired by evolution and a person's experience to make the best statistical guess about objects in a visual scene, based on how successful those guesses have been in the past.
"So, vision is not about extracting features from a scene; it's about extracting statistics in the sense of relating the image on your retina to the visually guided behavior that's worked in the past," said Purves. "This framework for thinking about vision explains quantitatively -- sometimes in amazing detail -- what we end up seeing."
In 2003, Purves and colleague Beau Lotto published an explanation of their "probabilistic" theory of vision in their book "Why We See What We Do: An Empirical Theory of Vision" (Sinauer Associates, Inc).
These two books and dozens of scientific papers have framed the questions that Purves believes researchers must ask about how vision works. But he emphasizes that those questions have only begun to be addressed in neurobiological terms.
"The problem for colleagues in physiology and anatomy is that our theory runs counter to what they've been doing for the last fifty years," said Purves. "And their response has understandably been 'Well, OK, that's interesting. But how do you relate this concept of vision to physiology and this anatomy?' It's perfectly valid to say, 'You've got a nice idea and it does explain the phenomenology of what we see, but how does that relate to the neurons that we know and love?'
"The answer is, we don't know," said Purves. "That's going to be the next many years of vision research. It will mean constructing a framework that explains how neurons and the connections among them operate in service of this complex, evolved statistical process called vision.
"Some bright people will certainly do this in the next ten, twenty or thirty years," said Purves. "I don't expect to be around to see it, but inevitably that will happen. But it's going to take people who deeply understand statistics and computer models of neural systems to develop a working theory of how the properties of neurons and anatomical connections are related to the end product of vision."
Purves said he hopes that the latest book that Catherine Howe and he have written, along with the earlier work, will continue the process of enlisting fellow neurobiologists in tackling the immense question of how we perceive the confusingly ambiguous visual world around us.